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          <h1 class="post-title" itemprop="name headline">python课程记录-12</h1>
        

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        <p>这一课讲Pandas库。</p>
<p><code>import pandas as pd</code></p>
<a id="more"></a>
<h2 id="Pandas初探"><a href="#Pandas初探" class="headerlink" title="Pandas初探"></a>Pandas初探</h2><ol>
<li>读取文件：<code>data = pd.read_excel(filename, index_col=0)</code></li>
<li>取出数据中的一列或一行：<code>data[&#39;学号&#39;], data.loc[1]</code></li>
</ol>
<h2 id="Pandas数据类型"><a href="#Pandas数据类型" class="headerlink" title="Pandas数据类型"></a>Pandas数据类型</h2><h3 id="Series-一维序列"><a href="#Series-一维序列" class="headerlink" title="Series(一维序列)"></a>Series(一维序列)</h3><ol>
<li><p>由index+value组成</p>
</li>
<li><p>通过列表创建Series：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br></pre></td><td class="code"><pre><span class="line">a = pd.Series([<span class="string">'apple'</span>,<span class="string">'peach'</span>])</span><br><span class="line"><span class="comment"># 0	apple</span></span><br><span class="line"><span class="comment"># 1	peach</span></span><br><span class="line">a.index</span><br><span class="line"><span class="comment"># RangeIndex(start=0,stop=2,step=1)</span></span><br><span class="line">a.values</span><br><span class="line"><span class="comment"># array(['apple','peach'],dtype=object)</span></span><br><span class="line">a[<span class="number">1</span>]</span><br><span class="line"><span class="comment"># 'peach'</span></span><br><span class="line">a[<span class="number">1</span>]=<span class="number">100</span></span><br><span class="line"><span class="comment"># 原来是peach的地方改成了100</span></span><br><span class="line">a[<span class="number">0</span>:]</span><br><span class="line"><span class="comment"># 整个输出</span></span><br><span class="line">a[[<span class="number">0</span>,<span class="number">1</span>]]</span><br><span class="line"><span class="comment"># 输出0和1</span></span><br><span class="line">a = pd.Series([<span class="string">'apple'</span>,<span class="string">'peach'</span>],index=[<span class="string">'a'</span>,<span class="string">'p'</span>])</span><br><span class="line"><span class="comment"># a    apple</span></span><br><span class="line"><span class="comment"># p    peach</span></span><br><span class="line">a[<span class="string">'a'</span>]</span><br><span class="line"><span class="comment"># 输出索引a对应的value</span></span><br><span class="line">a[<span class="string">'a'</span>]=<span class="string">'lemon'</span></span><br><span class="line"><span class="comment"># 原来的apple改成lemon</span></span><br><span class="line">a[<span class="string">'a'</span>:]</span><br><span class="line"><span class="comment"># 整个输出</span></span><br><span class="line">a[[<span class="string">'a'</span>,<span class="string">'p'</span>]]</span><br><span class="line"><span class="comment"># 输出a和p对应的value</span></span><br></pre></td></tr></table></figure>
</li>
<li><p>通过字典创建Series：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">d = &#123;<span class="string">'Japan'</span>:<span class="string">'Tokyo'</span>,<span class="string">'S.Korea'</span>:<span class="string">'Seoul'</span>,<span class="string">'China'</span>:<span class="string">'Beijing'</span>&#125;</span><br><span class="line">a = pd.Series(d)</span><br><span class="line"><span class="comment"># Japan        Tokyo</span></span><br><span class="line"><span class="comment"># S.Korea      Seoul</span></span><br><span class="line"><span class="comment"># China      Beijing</span></span><br><span class="line"></span><br><span class="line">indexL=[<span class="string">'China'</span>,<span class="string">'Japan'</span>,<span class="string">'ingapore'</span>,<span class="string">'S.Korea'</span>]</span><br><span class="line">a = pd.Series(d, index=indexL)</span><br><span class="line"><span class="comment"># China       Beijing</span></span><br><span class="line"><span class="comment"># Japan         Tokyo</span></span><br><span class="line"><span class="comment"># ingapore        NaN</span></span><br><span class="line"><span class="comment"># S.Korea       Seoul</span></span><br></pre></td></tr></table></figure>
</li>
<li><p>通过标量创建Series：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">a = pd.Series(<span class="string">'无'</span>)</span><br><span class="line"><span class="comment"># 0    无</span></span><br><span class="line">a = pd.Series(<span class="string">'无'</span>,index=np.arange(<span class="number">1</span>,<span class="number">6</span>))</span><br><span class="line"><span class="comment"># 1    无</span></span><br><span class="line"><span class="comment"># 2    无</span></span><br><span class="line"><span class="comment"># 3    无</span></span><br><span class="line"><span class="comment"># 4    无</span></span><br><span class="line"><span class="comment"># 5    无</span></span><br></pre></td></tr></table></figure>
</li>
<li><p>Series的基本运算：</p>
<ol>
<li>和numpy一样，过滤、广播、ufunc等</li>
<li>对齐：对应索引进行运算</li>
<li>频数统计：value_counts()</li>
</ol>
</li>
</ol>
<h3 id="DataFrame-二维表"><a href="#DataFrame-二维表" class="headerlink" title="DataFrame(二维表)"></a>DataFrame(二维表)</h3><ol>
<li><p>共用index的Series的有序集合</p>
</li>
<li><p>从二维数组创建：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">data = np.array([(<span class="string">'Japan'</span>,<span class="string">'Tokyo'</span>,<span class="number">4000</span>),(<span class="string">'S.Korea'</span>,<span class="string">'Seoul'</span>,<span class="number">1300</span>),(<span class="string">'China'</span>,<span class="string">'Beijing'</span>,<span class="number">9100</span>)])</span><br><span class="line">DF1 = pd.DataFrame(data, columns=[<span class="string">'nation'</span>,<span class="string">'capital'</span>,<span class="string">'GDP'</span>],index=[<span class="string">'a'</span>,<span class="string">'b'</span>,<span class="string">'c'</span>])</span><br></pre></td></tr></table></figure>
</li>
<li><p>查看索引和数据</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">DF1.index</span><br><span class="line">DF1.columns</span><br><span class="line">DF1.values</span><br></pre></td></tr></table></figure>
</li>
<li><p>从字典创建：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">dct = &#123;<span class="string">'nation'</span>:[<span class="string">'Japan'</span>,<span class="string">'S.Korea'</span>,<span class="string">'Japan'</span>],<span class="string">'capital'</span>:pd.Series([<span class="string">'Tokyo'</span>,<span class="string">'Seoul'</span>,<span class="string">'Beijing'</span>],index=[<span class="string">'a'</span>,<span class="string">'b'</span>,<span class="string">'c'</span>]),<span class="string">'GDP'</span>:np.array([<span class="number">4900.1300</span><span class="number">.9100</span>],dtype=int)&#125;</span><br><span class="line">DF2=pd.DataFrame(dct)</span><br></pre></td></tr></table></figure>
</li>
<li><p>把某列数据作为index：<code>DF2.set_index(&#39;nation&#39;)</code></p>
</li>
<li><p>修改index/columns：<code>DF2.reindex(index=[&#39;c&#39;,&#39;a&#39;,&#39;b&#39;,&#39;d&#39;])</code></p>
</li>
<li><p>数据选择：</p>
<ol>
<li>取头尾若干行：<code>df.head(行数), df.tail(行数)</code></li>
<li>选择一列：<code>DF2.nation, DF2[&#39;GDP&#39;]</code></li>
<li>选择一行或多行：<code>DF2[0:2], DF2[&#39;a&#39;:&#39;c&#39;]</code>    序号只能是切片，不能是下标</li>
<li><code>loc[index, columns]</code>根据索引对多个轴进行选取</li>
<li>取单个数据：<code>DF2.loc[&#39;c&#39;,&#39;GDP&#39;]</code>或<code>DF2.at[&#39;c&#39;,&#39;GDP&#39;]</code></li>
<li>布尔索引：<ol>
<li>用某列的值来选取数据：<code>DF2[DF2.GDP&gt;3000]</code></li>
<li><code>isin()</code>方法过滤数据：<code>DF2[DF2.nation.isin([&#39;China&#39;,&#39;S.Korea&#39;])]</code></li>
</ol>
</li>
</ol>
</li>
<li><p>增加一列：<code>DF2[&#39;population&#39;]=[130,55,1600]</code></p>
</li>
<li><p>增加行：<code>append(要添加的行)</code>，序号是添加行的name，如果加入参数ignore_index=True，则序号就是数字</p>
</li>
<li><p>删除行列：<code>drop(序号)</code>，根据索引删除行列，默认删行，axis=1是删列</p>
</li>
<li><p>多个DataFrame对象数据拼接：</p>
<ol>
<li><code>pd.concat([p1,p2])</code>，序号是p1和p2各自的序号直接拼起来，是前几行p1，后几行p2；增加参数ignore_index=True，则序号是0、1、2、3这样；增加参数axis=1，则是前几列p1，后几列p2。</li>
<li><code>pd.merge(p1,p3,on=&#39;name&#39;)</code>，把p1和p3中name列相同的部分融合起来，如果是要把所有name都留下，但是只要一列name，则增加参数<code>how=&#39;outer&#39;</code></li>
</ol>
</li>
<li><p>缺失值处理：</p>
<ol>
<li><code>isnull()</code>和<code>notnull()</code>：是否缺失</li>
<li><code>fillna()</code>：补充缺失值</li>
<li><code>dropna()</code>：删除包含缺失值的行或列</li>
</ol>
</li>
<li><p>基本运算：<code>sub,add,mul,div,sum,min,max,mean,std,describe</code>，默认是竖着运算，加axis=1变成横着运算</p>
</li>
<li><p>分组：<code>groupby()</code>、<code>get_group(列名)</code>，也可以进行上一条的基本运算</p>
</li>
<li><p>排序：<code>sort_value(by=&#39;&#39;, ascending=False)</code> 根据by的取值排序，可以是一个字符串<code>by=&#39;成绩&#39;</code>，也可以是多个字符串的列表，<code>by=[&#39;成绩&#39;,&#39;年级&#39;]</code></p>
</li>
</ol>
<h2 id="文件读写"><a href="#文件读写" class="headerlink" title="文件读写"></a>文件读写</h2><h3 id="csv"><a href="#csv" class="headerlink" title="csv"></a>csv</h3><ol>
<li><code>read_csv()/to_csv()</code>读写csv和txt</li>
<li>加入参数index_col=0可以没有序号</li>
</ol>
<h3 id="excel"><a href="#excel" class="headerlink" title="excel"></a>excel</h3><ol>
<li><p><code>read_excel()/to_excel()</code>读写excel</p>
</li>
<li><p>把几个dataframe写到一个excel文件里的不同sheet中：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">writer=pd.ExcelWriter(<span class="string">'atest.xlsx'</span>)</span><br><span class="line">a.to_excel(writer, sheet_name=<span class="string">'Sheet1'</span>)</span><br><span class="line">b.to_excel(writer, sheet_name=<span class="string">'Sheet2'</span>)</span><br><span class="line"><span class="comment"># 或者</span></span><br><span class="line"><span class="keyword">with</span> pd.ExcelWriter(<span class="string">'atest.xlsx'</span>) <span class="keyword">as</span> writer:</span><br><span class="line">    a.to_excel(writer, sheet_name=<span class="string">'Sheet1'</span>)</span><br><span class="line">	b.to_excel(writer, sheet_name=<span class="string">'Sheet2'</span>)</span><br></pre></td></tr></table></figure>
</li>
<li><p>读取一个exce里的多个sheet</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">reader = pd.ExcelWriter(<span class="string">'atest.xlsx'</span>)</span><br><span class="line">df1 = pd.read_excel(reader, <span class="string">'Sheet1'</span>)</span><br><span class="line">df2 = pd.read_excel(reader, <span class="string">'Sheet2'</span>)</span><br><span class="line"><span class="comment"># 或者</span></span><br><span class="line"><span class="keyword">with</span> pd.ExcelWriter(<span class="string">'atest.xlsx'</span>) <span class="keyword">as</span> reader:</span><br><span class="line">    df1 = pd.read_excel(reader, <span class="string">'Sheet1'</span>)</span><br><span class="line">	df2 = pd.read_excel(reader, <span class="string">'Sheet2'</span>)</span><br></pre></td></tr></table></figure>
</li>
</ol>
<h2 id="绘图"><a href="#绘图" class="headerlink" title="绘图"></a>绘图</h2><ol>
<li>matplotlib中的pyplot模块：<code>b.plot()</code>，表格中的一列代表一条线的数据，行名是横坐标，表格内容值是纵坐标，默认是折线图</li>
<li>柱状图：<code>b.plot(kind=&#39;bar&#39;)      b.plot.bar()</code></li>
</ol>
<h2 id="随堂练习"><a href="#随堂练习" class="headerlink" title="随堂练习"></a>随堂练习</h2><p> 读入第三次作业第一部分爬虫得到的 csv文件，之后：</p>
<ol>
<li>绘制浏览次数曲线图；</li>
<li>根据浏览次数进行降序排序，并打印前10篇报道的日期、标题； </li>
<li>求浏览次数为1的报道的链接并打印结果；</li>
<li>统计每一年的报道数量，以及每一年的总的浏览次数，并分别以 柱状图的形式绘制出来；</li>
<li>统计2019年每个月的报道数量并以饼图的形式绘制出来。</li>
</ol>
<h3 id="思路"><a href="#思路" class="headerlink" title="思路"></a>思路</h3><ol>
<li><p>首先加载库文件：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br></pre></td></tr></table></figure>
</li>
<li><p>接下来读取csv文件，考虑到后面的几项要求，把日期作为DatetimeIndex的索引比较好：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">reports = pd.read_csv(<span class="string">'reportsInfo.csv'</span>, header=<span class="number">0</span>, index_col=<span class="number">0</span>, parse_dates=<span class="literal">True</span>)</span><br></pre></td></tr></table></figure>
</li>
<li><p>第一个要求是绘制浏览次数的折线图，因为原本的csv文件时间是先2020年再2019年然后2018年这样倒着来的，所以先重新排序，再取浏览次数那一列的数值来画图：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">reports.sort_index()[<span class="string">'浏览次数'</span>].plot(linestyle=<span class="string">'-'</span>, linewidth=<span class="number">2</span>, color=<span class="string">'steelblue'</span>)</span><br><span class="line">plt.title(<span class="string">'浏览次数折线图'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.xlabel(<span class="string">'日期'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.ylabel(<span class="string">'浏览次数'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.tight_layout()</span><br><span class="line">plt.savefig(<span class="string">'reports_linechart'</span>, dpi=<span class="number">300</span>)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
</li>
<li><p>根据浏览次数降序排序的操作和上一步索引排序差不多，然后切片取前10，用loc定位到标题那一列，输出即可：</p>
<p><code>print(reports.sort_values(by=&#39;浏览次数&#39;, ascending=False)[0:10].loc[:, [&#39;标题&#39;]])</code></p>
</li>
<li><p>筛选浏览次数为1的链接，用下标的方式找即可：</p>
<p><code>print(reports[reports.浏览次数 == 1].链接)</code></p>
</li>
<li><p>统计每年的报道数量，需要先按年来分组，然后得到分组的size，对size画图即可：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">reports_group = reports.groupby(reports.index.year)</span><br><span class="line">reports_group.size().plot.bar()</span><br><span class="line">plt.title(<span class="string">'年报道数量柱状图'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.xlabel(<span class="string">'年份'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.ylabel(<span class="string">'报道数量'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.tight_layout()</span><br><span class="line">plt.xticks(rotation=<span class="number">0</span>)</span><br><span class="line">plt.savefig(<span class="string">'reports_bar_1'</span>, dpi=<span class="number">300</span>)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
</li>
<li><p>统计每年的总浏览次数，在上一条分组的基础上对浏览次数那一列求和，然后画图即可：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">reports_group[<span class="string">'浏览次数'</span>].sum().plot.bar()</span><br><span class="line">plt.title(<span class="string">'年浏览次数柱状图'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.xlabel(<span class="string">'年份'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.ylabel(<span class="string">'浏览次数'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.tight_layout()</span><br><span class="line">plt.xticks(rotation=<span class="number">0</span>)</span><br><span class="line">plt.savefig(<span class="string">'reports_bar_2'</span>, dpi=<span class="number">300</span>)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
</li>
<li><p>统计2019年每月的报道数量，在之前的分组中可以得到2019的分组，然后对2019部分再按月来分组，得到size，对size画图即可：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">reports_2019 = reports_group.get_group(<span class="number">2019</span>)</span><br><span class="line">reports_2019_month = reports_2019.groupby(reports_2019.index.month).size()</span><br><span class="line">reports_2019_month.name = <span class="string">''</span></span><br><span class="line">reports_2019_month.plot.pie(startangle=<span class="number">90</span>)</span><br><span class="line">plt.title(<span class="string">'2019年每月报道数量饼状图'</span>, fontproperties=<span class="string">'Kaiti'</span>)</span><br><span class="line">plt.savefig(<span class="string">'reports_pie'</span>, dpi=<span class="number">300</span>)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure></li>
</ol>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#Pandas初探"><span class="nav-number">1.</span> <span class="nav-text">Pandas初探</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Pandas数据类型"><span class="nav-number">2.</span> <span class="nav-text">Pandas数据类型</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Series-一维序列"><span class="nav-number">2.1.</span> <span class="nav-text">Series(一维序列)</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#DataFrame-二维表"><span class="nav-number">2.2.</span> <span class="nav-text">DataFrame(二维表)</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#文件读写"><span class="nav-number">3.</span> <span class="nav-text">文件读写</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#csv"><span class="nav-number">3.1.</span> <span class="nav-text">csv</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#excel"><span class="nav-number">3.2.</span> <span class="nav-text">excel</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#绘图"><span class="nav-number">4.</span> <span class="nav-text">绘图</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#随堂练习"><span class="nav-number">5.</span> <span class="nav-text">随堂练习</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#思路"><span class="nav-number">5.1.</span> <span class="nav-text">思路</span></a></li></ol></li></ol></div>
            

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                    index.sort(function (itemLeft, itemRight) {
                      if (itemRight.position !== itemLeft.position) {
                        return itemRight.position - itemLeft.position;
                      } else {
                        return itemLeft.word.length - itemRight.word.length;
                      }
                    });
                  });

                  // merge hits into slices

                  function mergeIntoSlice(text, start, end, index) {
                    var item = index[index.length - 1];
                    var position = item.position;
                    var word = item.word;
                    var hits = [];
                    var searchTextCountInSlice = 0;
                    while (position + word.length <= end && index.length != 0) {
                      if (word === searchText) {
                        searchTextCountInSlice++;
                      }
                      hits.push({position: position, length: word.length});
                      var wordEnd = position + word.length;

                      // move to next position of hit

                      index.pop();
                      while (index.length != 0) {
                        item = index[index.length - 1];
                        position = item.position;
                        word = item.word;
                        if (wordEnd > position) {
                          index.pop();
                        } else {
                          break;
                        }
                      }
                    }
                    searchTextCount += searchTextCountInSlice;
                    return {
                      hits: hits,
                      start: start,
                      end: end,
                      searchTextCount: searchTextCountInSlice
                    };
                  }

                  var slicesOfTitle = [];
                  if (indexOfTitle.length != 0) {
                    slicesOfTitle.push(mergeIntoSlice(title, 0, title.length, indexOfTitle));
                  }

                  var slicesOfContent = [];
                  while (indexOfContent.length != 0) {
                    var item = indexOfContent[indexOfContent.length - 1];
                    var position = item.position;
                    var word = item.word;
                    // cut out 100 characters
                    var start = position - 20;
                    var end = position + 80;
                    if(start < 0){
                      start = 0;
                    }
                    if (end < position + word.length) {
                      end = position + word.length;
                    }
                    if(end > content.length){
                      end = content.length;
                    }
                    slicesOfContent.push(mergeIntoSlice(content, start, end, indexOfContent));
                  }

                  // sort slices in content by search text's count and hits' count

                  slicesOfContent.sort(function (sliceLeft, sliceRight) {
                    if (sliceLeft.searchTextCount !== sliceRight.searchTextCount) {
                      return sliceRight.searchTextCount - sliceLeft.searchTextCount;
                    } else if (sliceLeft.hits.length !== sliceRight.hits.length) {
                      return sliceRight.hits.length - sliceLeft.hits.length;
                    } else {
                      return sliceLeft.start - sliceRight.start;
                    }
                  });

                  // select top N slices in content

                  var upperBound = parseInt('1');
                  if (upperBound >= 0) {
                    slicesOfContent = slicesOfContent.slice(0, upperBound);
                  }

                  // highlight title and content

                  function highlightKeyword(text, slice) {
                    var result = '';
                    var prevEnd = slice.start;
                    slice.hits.forEach(function (hit) {
                      result += text.substring(prevEnd, hit.position);
                      var end = hit.position + hit.length;
                      result += '<b class="search-keyword">' + text.substring(hit.position, end) + '</b>';
                      prevEnd = end;
                    });
                    result += text.substring(prevEnd, slice.end);
                    return result;
                  }

                  var resultItem = '';

                  if (slicesOfTitle.length != 0) {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + highlightKeyword(title, slicesOfTitle[0]) + "</a>";
                  } else {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x" /></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'auto') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  

  
  

  
  
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  <style>
    .copy-btn {
      display: inline-block;
      padding: 6px 12px;
      font-size: 13px;
      font-weight: 700;
      line-height: 20px;
      color: #333;
      white-space: nowrap;
      vertical-align: middle;
      cursor: pointer;
      background-color: #eee;
      background-image: linear-gradient(#fcfcfc, #eee);
      border: 1px solid #d5d5d5;
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      user-select: none;
      outline: 0;
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    .highlight-wrap .copy-btn {
      transition: opacity .3s ease-in-out;
      opacity: 0;
      padding: 2px 6px;
      position: absolute;
      right: 4px;
      top: 8px;
    }

    .highlight-wrap:hover .copy-btn,
    .highlight-wrap .copy-btn:focus {
      opacity: 1
    }

    .highlight-wrap {
      position: relative;
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  <script>
    $('.highlight').each(function (i, e) {
      var $wrap = $('<div>').addClass('highlight-wrap')
      $(e).after($wrap)
      $wrap.append($('<button>').addClass('copy-btn').append('Copy').on('click', function (e) {
        var code = $(this).parent().find('.code').find('.line').map(function (i, e) {
          return $(e).text()
        }).toArray().join('\n')
        var ta = document.createElement('textarea')
        document.body.appendChild(ta)
        ta.style.position = 'absolute'
        ta.style.top = '0px'
        ta.style.left = '0px'
        ta.value = code
        ta.select()
        ta.focus()
        var result = document.execCommand('copy')
        document.body.removeChild(ta)
        
          if(result)$(this).text('Success')
          else $(this).text('Fail')
        
        $(this).blur()
      })).on('mouseleave', function (e) {
        var $b = $(this).find('.copy-btn')
        setTimeout(function () {
          $b.text('Copy')
        }, 300)
      }).append(e)
    })
  </script>

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